Pub Date : 2023-11-10DOI: 10.1007/s11760-023-02865-9
Saman M. Omer, Kayhan Z. Ghafoor, Shavan K. Askar
{"title":"Lightweight improved yolov5 model for cucumber leaf disease and pest detection based on deep learning","authors":"Saman M. Omer, Kayhan Z. Ghafoor, Shavan K. Askar","doi":"10.1007/s11760-023-02865-9","DOIUrl":"https://doi.org/10.1007/s11760-023-02865-9","url":null,"abstract":"","PeriodicalId":54393,"journal":{"name":"Signal Image and Video Processing","volume":"120 50","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Rolling bearing fault diagnosis in strong noise background based on vibration signals","authors":"Dongjie Li, Mingyue Li, Liu Yang, Xueying Wang, Fuyue Zhang, Yu Liang","doi":"10.1007/s11760-023-02846-y","DOIUrl":"https://doi.org/10.1007/s11760-023-02846-y","url":null,"abstract":"","PeriodicalId":54393,"journal":{"name":"Signal Image and Video Processing","volume":"3 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1007/s11760-023-02824-4
Weina Zhou, Xiu Wang
{"title":"Twin-stage Unet-like network for single image deraining","authors":"Weina Zhou, Xiu Wang","doi":"10.1007/s11760-023-02824-4","DOIUrl":"https://doi.org/10.1007/s11760-023-02824-4","url":null,"abstract":"","PeriodicalId":54393,"journal":{"name":"Signal Image and Video Processing","volume":"43 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135432897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-04DOI: 10.1007/s11760-023-02821-7
Rafael Faria Lopes, Joana Gonçalves-Ribeiro, Ana M. Sebastião, Carlos Meneses, Sandra H. Vaz
Abstract Astrocytes are non-neural cells, restricted to the brain and spinal cord, whose functions and morphology depend on their location. Astrocyte–astrocyte and astrocyte–neuron interactions occur through cytoplasmic Ca 2+ level changes that are assessed to determine cell function and response (i.e., drug testing). The evaluation of alterations in intracellular Ca 2+ levels primarily relies on fluorescence imaging techniques, performed through video recording of cells incubated with Ca 2+ -sensitive dyes. By observing ion concentration shifts over time in a delimited region of interest (ROI) encompassing a single cell, it is possible to draw conclusions on cell responses to specific stimuli. Our work describes a tool named SIGAA — signaling automated analysis , for astrocyte ROI-based fluorescent imaging. This tool is specifically tailored for two wavelengths excited dyes by using two inputs of Ca 2+ signaling recorded frames/videos and outputting a set of features relevant to the experiment’s conclusions and cell characterization. SIGAA performs automatic drift correction for the two recorded videos with a template matching algorithm, followed by astrocyte identification (ROI) using morphological reconstruction techniques. Subsequently, SIGAA extracts intracellular Ca 2+ evolution functions for all identified ROIs detects function transients, and estimates a set of features for each signal. These features closely resemble those obtained through traditional methods and software used thus far. SIGAA is a new fully automated tool, which can speed up hour-long studies and analysis to a few minutes, showing reliable results as the validity tests indicate.
{"title":"SIGAA: signaling automated analysis: a new tool for Ca2+ signaling quantification using ratiometric Ca2+ dyes","authors":"Rafael Faria Lopes, Joana Gonçalves-Ribeiro, Ana M. Sebastião, Carlos Meneses, Sandra H. Vaz","doi":"10.1007/s11760-023-02821-7","DOIUrl":"https://doi.org/10.1007/s11760-023-02821-7","url":null,"abstract":"Abstract Astrocytes are non-neural cells, restricted to the brain and spinal cord, whose functions and morphology depend on their location. Astrocyte–astrocyte and astrocyte–neuron interactions occur through cytoplasmic Ca 2+ level changes that are assessed to determine cell function and response (i.e., drug testing). The evaluation of alterations in intracellular Ca 2+ levels primarily relies on fluorescence imaging techniques, performed through video recording of cells incubated with Ca 2+ -sensitive dyes. By observing ion concentration shifts over time in a delimited region of interest (ROI) encompassing a single cell, it is possible to draw conclusions on cell responses to specific stimuli. Our work describes a tool named SIGAA — signaling automated analysis , for astrocyte ROI-based fluorescent imaging. This tool is specifically tailored for two wavelengths excited dyes by using two inputs of Ca 2+ signaling recorded frames/videos and outputting a set of features relevant to the experiment’s conclusions and cell characterization. SIGAA performs automatic drift correction for the two recorded videos with a template matching algorithm, followed by astrocyte identification (ROI) using morphological reconstruction techniques. Subsequently, SIGAA extracts intracellular Ca 2+ evolution functions for all identified ROIs detects function transients, and estimates a set of features for each signal. These features closely resemble those obtained through traditional methods and software used thus far. SIGAA is a new fully automated tool, which can speed up hour-long studies and analysis to a few minutes, showing reliable results as the validity tests indicate.","PeriodicalId":54393,"journal":{"name":"Signal Image and Video Processing","volume":"13 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135773367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-03DOI: 10.1007/s11760-023-02759-w
Haipeng Zhang, Jianzhou Wang, Qiwei Li
{"title":"Nonlinear fuzzy forecasting system for wind speed interval forecasting based on self-adaption feature selecting and Bi-LSTM","authors":"Haipeng Zhang, Jianzhou Wang, Qiwei Li","doi":"10.1007/s11760-023-02759-w","DOIUrl":"https://doi.org/10.1007/s11760-023-02759-w","url":null,"abstract":"","PeriodicalId":54393,"journal":{"name":"Signal Image and Video Processing","volume":"2 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Accurate microscopic images segmentation of activated sludge is essential for monitoring wastewater treatment processes. However, it is a challenging task due to poor contrast, artifacts, morphological similarities, and distribution imbalance. A novel image segmentation model (FafFormer) was developed in the work based on Transformer that incorporated pyramid pooling and flow alignment fusion. Pyramid Pooling Module was used to extract multi-scale features of flocs and filamentous bacteria with different morphology in the encoder. Multi-scale features were fused by flow alignment fusion module in the decoder. The module used generated semantic flow as auxiliary information to restore boundary details and facilitate fine-grained upsampling. The Focal–Lovász Loss was designed to handle class imbalance for filamentous bacteria and flocs. Image-segmentation experiments were conducted on an activated sludge dataset from a municipal wastewater treatment plant. FafFormer showed relative superiority in accuracy and reliability, especially for filamentous bacteria compared to existing models.
{"title":"Multi-scale feature flow alignment fusion with Transformer for the microscopic images segmentation of activated sludge","authors":"Lijie Zhao, Yingying Zhang, Guogang Wang, Mingzhong Huang, Qichun Zhang, Hamid Reza Karimi","doi":"10.1007/s11760-023-02836-0","DOIUrl":"https://doi.org/10.1007/s11760-023-02836-0","url":null,"abstract":"Abstract Accurate microscopic images segmentation of activated sludge is essential for monitoring wastewater treatment processes. However, it is a challenging task due to poor contrast, artifacts, morphological similarities, and distribution imbalance. A novel image segmentation model (FafFormer) was developed in the work based on Transformer that incorporated pyramid pooling and flow alignment fusion. Pyramid Pooling Module was used to extract multi-scale features of flocs and filamentous bacteria with different morphology in the encoder. Multi-scale features were fused by flow alignment fusion module in the decoder. The module used generated semantic flow as auxiliary information to restore boundary details and facilitate fine-grained upsampling. The Focal–Lovász Loss was designed to handle class imbalance for filamentous bacteria and flocs. Image-segmentation experiments were conducted on an activated sludge dataset from a municipal wastewater treatment plant. FafFormer showed relative superiority in accuracy and reliability, especially for filamentous bacteria compared to existing models.","PeriodicalId":54393,"journal":{"name":"Signal Image and Video Processing","volume":"107 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1007/s11760-023-02826-2
Muhammed Yildirim, Soner Kiziloluk, Serpil Aslan, Eser Sert
{"title":"A new hybrid approach based on AOA, CNN and feature fusion that can automatically diagnose Parkinson's disease from sound signals: PDD-AOA-CNN","authors":"Muhammed Yildirim, Soner Kiziloluk, Serpil Aslan, Eser Sert","doi":"10.1007/s11760-023-02826-2","DOIUrl":"https://doi.org/10.1007/s11760-023-02826-2","url":null,"abstract":"","PeriodicalId":54393,"journal":{"name":"Signal Image and Video Processing","volume":"22 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135270766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：